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About this product
- DescriptionThis book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics t rmally included in books on categorical data analysis are treated here, such as nparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and n-standard tree-based ensemble methods, which provide excellent tools for prediction and the handling of both minal and ordered categorical predictors. The book is accompanied by an R package that contains data sets and code for all the examples.
- Author BiographyDr Gerhard Tutz is a Professor of Mathematics in the Department of Statistics at Ludwig-Maximilians University, Munich. He is formerly a Professor at the Technical University Berlin. He is the author or co-author of nine books and more than 100 papers.
- Author(s)Gerhard Tutz
- PublisherCambridge University Press
- Date of Publication21/11/2011
- Series TitleCambridge Series in Statistical and Probabilistic Mathematics
- Series Part/Volume Number34
- Place of PublicationCambridge
- Country of PublicationUnited Kingdom
- ImprintCambridge University Press
- Content Note98 b/w illus. 102 tables 77 exercises
- Weight1160 g
- Width215 mm
- Height253 mm
- Spine35 mm
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